这个框架让做模型微调或安全对齐的团队省去手动设计编辑算法的时间，直接用 RL 学习策略就能完成偏见缓解或遗忘任务，值得关注。
该论文提出一个探索性框架，将神经网络模型编辑形式化为强化学习问题，代理通过奖励反馈修改模型参数。框架包含两个环境：MaskWorld（乘法缩放权重）和 ShiftWorld（加法更新权重），奖励函数结合效用保持和任务特定编辑目标。在文本分类的偏见缓解和图像分类的机器遗忘任务上，学习到的策略在遗忘任务中将遗忘集准确率降至接近 0%，同时保留集准确率超过 90%；在偏见缓解中，偏见相关性能提升超 5% 且保持分类效用。结果表明，模型编辑可通过 RL 学习策略，无需为每个任务手动设计专用算法。
Reinforcement Learning for Neural Model Editing
Editing pretrained neural networks requires specialized algorithms tailored to specific objectives. Designing such algorithms is often time-consuming and demands significant effort. We present an exploratory framework that formulates neural model editing as a reinforcement learning problem, where agents modify models using reward feedback. We introduce two environments: MaskWorld, where agents scale weights multiplicatively, and ShiftWorld, where agents apply additive weight updates. The reward function combines a utility-preservation objective with a task-specific editing objective, enabling agents to learn targeted modifications while maintaining overall model performance. We evaluate the framework on bias mitigation in text classification and machine unlearning in image classification, both of which traditionally rely on specialized algorithms. Our results show that the learned policies reduce forget set accuracy to nearly 0% while preserving over 90% retain set accuracy on the unlearning task. In the bias mitigation setting, the learned policies improve bias-related performance by more than 5% while maintaining general classification utility. Our findings show that neural model editing can be cast as a reinforcement learning problem, allowing editing policies to be learned from reward feedback rather than manually engineered for each task.